Evaluating Generative Systems Without Ground Truth

In traditional software development, regression tests are binary: function $f(x)$ either equals $y$ or fails. In generative and applied intelligence software, an update might improve phrasing or enhance clarity while remaining factually sound—or conversely, introduce subtle conceptual inaccuracies that bypass superficial checks.

When a single definitive ground truth does not exist, how do you continuously evaluate system quality?

Triangulated Continuous Testing

We test generative pipelines using three complementary dimensions:

1. Adversarial Counterfactuals: Perturbing numerical values and premises in real query traces. If the system produces the same conclusion despite inverted premises, it uncovers ungrounded generation.
2. Deterministic Constraint Verification: Enforcing that generated output strictly respects structural, schema, and safety invariant rules.
3. Multi-Perspective Consistency: Comparing parallel generations across temperature variations to measure variance and epistemic uncertainty.

Treating quality as a multi-dimensional surface rather than a single scalar score enables teams to ship updates with confidence.